Package cascading.pattern.model.generalregression

Examples of cascading.pattern.model.generalregression.RegressionTable.addParameter()

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    String targetCategory = regressionTable.getTargetCategory();

    if( targetCategory != null )
      generalRegressionTable.setTargetCategory( targetCategory );

    generalRegressionTable.addParameter( new Parameter( "intercept", regressionTable.getIntercept() ) );

    int count = 0;

    for( CategoricalPredictor predictor : regressionTable.getCategoricalPredictors() )
      {
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      {
      String name = predictor.getName().getValue();
      String value = predictor.getValue();
      double coefficient = predictor.getCoefficient();

      generalRegressionTable.addParameter( new Parameter( "f" + count++, coefficient, new FactorPredictor( name, value ) ) );
      }

    for( NumericPredictor predictor : regressionTable.getNumericPredictors() )
      {
      String name = predictor.getName().getValue();
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      String name = predictor.getName().getValue();
      int exponent = predictor.getExponent();

      double coefficient = predictor.getCoefficient();

      generalRegressionTable.addParameter( new Parameter( "f" + count++, coefficient, new CovariantPredictor( name, exponent ) ) );
      }

    return generalRegressionTable;
    }
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      {
      String parameterName = modelPCell.getParameterName();
      double beta = modelPCell.getBeta();
      Integer df = modelPCell.getDf();

      regressionTable.addParameter( new cascading.pattern.model.generalregression.Parameter( parameterName, beta, df.intValue() ) );
      }

    for( org.dmg.pmml.PPCell modelPPCell : model.getPPMatrix().getPPCells() )
      {
      String parameterName = modelPPCell.getParameterName();
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    regressionSpec.setLinkFunction( LinkFunction.LOGIT );

    RegressionTable table = new RegressionTable();

    table.addParameter( new Parameter( "p0", -16.9456960387809d ) );
    table.addParameter( new Parameter( "p1", 11.7592159418536d, new CovariantPredictor( "sepal_length" ) ) );
    table.addParameter( new Parameter( "p2", 7.84157781514097d, new CovariantPredictor( "sepal_width" ) ) );
    table.addParameter( new Parameter( "p3", -20.0880078273996d, new CovariantPredictor( "petal_length" ) ) );
    table.addParameter( new Parameter( "p4", -21.6076488529538d, new CovariantPredictor( "petal_width" ) ) );
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    regressionSpec.setLinkFunction( LinkFunction.LOGIT );

    RegressionTable table = new RegressionTable();

    table.addParameter( new Parameter( "p0", -16.9456960387809d ) );
    table.addParameter( new Parameter( "p1", 11.7592159418536d, new CovariantPredictor( "sepal_length" ) ) );
    table.addParameter( new Parameter( "p2", 7.84157781514097d, new CovariantPredictor( "sepal_width" ) ) );
    table.addParameter( new Parameter( "p3", -20.0880078273996d, new CovariantPredictor( "petal_length" ) ) );
    table.addParameter( new Parameter( "p4", -21.6076488529538d, new CovariantPredictor( "petal_width" ) ) );

    regressionSpec.addRegressionTable( table );
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    RegressionTable table = new RegressionTable();

    table.addParameter( new Parameter( "p0", -16.9456960387809d ) );
    table.addParameter( new Parameter( "p1", 11.7592159418536d, new CovariantPredictor( "sepal_length" ) ) );
    table.addParameter( new Parameter( "p2", 7.84157781514097d, new CovariantPredictor( "sepal_width" ) ) );
    table.addParameter( new Parameter( "p3", -20.0880078273996d, new CovariantPredictor( "petal_length" ) ) );
    table.addParameter( new Parameter( "p4", -21.6076488529538d, new CovariantPredictor( "petal_width" ) ) );

    regressionSpec.addRegressionTable( table );
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    RegressionTable table = new RegressionTable();

    table.addParameter( new Parameter( "p0", -16.9456960387809d ) );
    table.addParameter( new Parameter( "p1", 11.7592159418536d, new CovariantPredictor( "sepal_length" ) ) );
    table.addParameter( new Parameter( "p2", 7.84157781514097d, new CovariantPredictor( "sepal_width" ) ) );
    table.addParameter( new Parameter( "p3", -20.0880078273996d, new CovariantPredictor( "petal_length" ) ) );
    table.addParameter( new Parameter( "p4", -21.6076488529538d, new CovariantPredictor( "petal_width" ) ) );

    regressionSpec.addRegressionTable( table );

    PredictionRegressionFunction regressionFunction = new PredictionRegressionFunction( regressionSpec );
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    table.addParameter( new Parameter( "p0", -16.9456960387809d ) );
    table.addParameter( new Parameter( "p1", 11.7592159418536d, new CovariantPredictor( "sepal_length" ) ) );
    table.addParameter( new Parameter( "p2", 7.84157781514097d, new CovariantPredictor( "sepal_width" ) ) );
    table.addParameter( new Parameter( "p3", -20.0880078273996d, new CovariantPredictor( "petal_length" ) ) );
    table.addParameter( new Parameter( "p4", -21.6076488529538d, new CovariantPredictor( "petal_width" ) ) );

    regressionSpec.addRegressionTable( table );

    PredictionRegressionFunction regressionFunction = new PredictionRegressionFunction( regressionSpec );
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    GeneralRegressionSpec regressionSpec = new GeneralRegressionSpec( modelSchema );

    RegressionTable regressionTable = new RegressionTable();

    regressionTable.addParameter( new Parameter( "intercept", 2.24166872421148d ) );

    regressionTable.addParameter( new Parameter( "p1", 0.53448203205212d, new CovariantPredictor( "sepal_width" ) ) );
    regressionTable.addParameter( new Parameter( "p2", 0.691035562908626d, new CovariantPredictor( "petal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p3", -0.21488157609202d, new CovariantPredictor( "petal_width" ) ) );
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